Driven by the rapid expansion of the Internet, a variety of data is becoming available more and more. Examples of such data are media content, e.g. digital video, digital audio and digital photos, devotional content, educational content, sports content, gaming content, e-reading content and fashion related content. From the enormous amount of data it is becoming difficult to find favorite data. E.g. with respect to digital audio, it is to be expected that at some point in time substantially all music will be available online, e.g. through music portal websites. With potentially billions of music tracks from new and existing artists being added to the worldwide online available music collection on a monthly time scale, it is becoming very difficult to find favorite music tracks or new music tracks to ones liking from the vast collection of music. Analogous growth in available content is seen for other types of data.
Single items of data, such as e.g. a music track, a video, a photo, a news item or informative item, a game, an e-book or an e-cartoon are examples of data elements. To enable searching for and/or selection of particular data element, data elements are typically provided with characterizing information in the form of meta-data. E.g. for music tracks the meta-data typically includes characteristics like artist, title, producer, genre, style, composer, year of release and/or any other characterizing information. Analogously, other types of data can be enriched with meta-data.
A playlist is a collection of data elements grouped together under a particular logic. The playlist can be created such to e.g. reflect a particular mood, accompany a particular activity (e.g. work, romance, sports), serve as background music, or to explore novel songs for music discoveries.
Playlist may be generated either automatically or manually. Known online music portals, such as Spotify (www.spotify.com) offer tools to make, share, and listen to playlists in the form of sequences of music tracks selected manually. Individual tracks in the playlist are selectable from an online library of music. Automatically created playlists typically contain data elements with matching or similar meta-data. Manually selection of a data element is typically driven by watching or listening to a particular data element, a recommendation of a data element or a preset playlist. It is possible that a user provides a manually selected data element, e.g. a music track, or particular meta-data, e.g. a music artist, as a query and that a playlist is generated automatically in response to this query. Such a service is often offered by online music radios such as Pandora (www.pandora.com), and Last.fm (www.last.fm).
A graphical user interface can support a user to select data elements to be included in a playlist. The graphical user interface is typically part of a web page, an online application, a software application for use on e.g. a PC, notebook, smart phone or tablet PC, or an application plug-in. Known graphical user interfaces for creating playlists are found to be either difficult to use, difficult to learn or requiring a lot of user interaction.
A visual playlist creation method is known from VAN GULIK R ET AL: “Visual Playlist Generation on the Artists Map”, PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MUSIC INFORMATION RETRIEVAL, ISMIR 2005, 11 Sep. 2005, pages 520-523, XP002613950, London. The method involves the drawing of a path using a mouse and/or clicking individual points of interest on the map and interconnecting these points to form the path. The drawn path is used as a basis for creating a playlist. A disadvantage of this method is that the user will have to spend lot of time to produce the desired shape of the path requiring lot of attempts to produce the playlist path. For example, using a mouse movement method (recording of the mouse movements within the visualization area while the left button is pressed) a user is able to decide on the starting point and the end point of the playlist path, but the playlist path drawn connecting the desired start point and the end point may not have the desired intermediate points. Thus the user will have to redraw the playlist path again to get the desired path of the intermediate points whilst remembering the start point and the end point of the previously attempted playlist path. The other method of clicking individual points of interest on the map is also not very user friendly since this method requires lot of time and effort to affix the points and then further connecting these points in a series of steps.
Moreover, the method disclosed by VAN GULIK R ET AL will be difficult to use when the desired playlist path is complex to draw when there are singular or multiple points of desired intersection(s) of the start/end/intermediate point(s) with the start/end/intermediate point(s) defining the playlist path, or when the desired play listing points (start point, end point, intermediate point(s)) are falling on the exact/near-exact/near to the outer boundary of the visual map. Every new attempt to produce the desired playlist path requires the user to erase the playlist path and redraw the playlist path. The user will have to repeat the steps until the user is able to produce a desired playlist path.
There is a need for a play listing method using a graphical user interface that addresses the above mentioned difficulties and offers a logical method which is easy to use and reproduces the desired playlist path. The playlisting path can be used for creating a playlist for different types of data from a vast and growing amount of available data elements.